Facemorph.Stats
Class ImageRegressor

java.lang.Object
  Facemorph.Stats.ImageRegressor

All Implemented Interfaces:

IniHandler, Regressor, StatisticalModel

public class ImageRegressor
extends java.lang.Object
implements Regressor

Nested Class Summary

Nested classes/interfaces inherited from interface Facemorph.Stats.Regressor

Regressor.RegressorException

Constructor Summary

ImageRegressor()

Method Summary

BigMat	apply(BigMat in) Apply the statistical model on the input data supplied in Matrix form
int	getInputDimensions()
void	getModelInformation(ModelInformation mi) Queries the statistical model about its abilities.
void	getModelInformation(ModelResults result) Query the (previously built) model for information about the model's components
int	getOutputDimensions()
double	getSumSquaredResiduals()
java.lang.StringBuffer	outputStatistics()
boolean	outputStatistics(java.io.File statsFile)
double[]	predict(BigMat values) Predict using the model the values of the dependent from a set of independents
boolean	read(java.io.File f)
boolean	read(iniFile in) Read from the current position in an iniFile.
void	setDependentData(BigMat data) The dependent part of the regression
void	setDependentData(double[] data) The dependent part of the regression
void	setIndependentData(BigMat data) The independent part of the regression
boolean	train(boolean stats) Perform multi-linear regression using the Ordinary Least Squares method.
boolean	write(java.io.File s) Writes to the file specified
boolean	write(iniFile file, java.lang.String name) Writes this Template to file (via a PrintStream)
boolean	write(java.io.PrintStream out) Writes this Template to file (via a PrintStream)

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

ImageRegressor

public ImageRegressor()

Method Detail

train

public boolean train(boolean stats)

Description copied from interface: StatisticalModel

Perform multi-linear regression using the Ordinary Least Squares method. Solves y = XB + e using b = inv(X'X)X'y

Specified by:

train in interface StatisticalModel

Parameters:

stats - calculate values for statistical analysis

Returns:

true if the regression succeeds.

predict

public double[] predict(BigMat values)

Description copied from interface: Regressor

Predict using the model the values of the dependent from a set of independents

Specified by:

predict in interface Regressor

Parameters:

values - the independent values

Returns:

set of predicted values

setIndependentData

public void setIndependentData(BigMat data)

Description copied from interface: StatisticalModel

The independent part of the regression

Specified by:

setIndependentData in interface StatisticalModel

setDependentData

public void setDependentData(BigMat data)

Description copied from interface: StatisticalModel

The dependent part of the regression

Specified by:

setDependentData in interface StatisticalModel

setDependentData

public void setDependentData(double[] data)

Description copied from interface: StatisticalModel

The dependent part of the regression

Specified by:

setDependentData in interface StatisticalModel

getSumSquaredResiduals

public double getSumSquaredResiduals()

Specified by:

getSumSquaredResiduals in interface Regressor

Returns:

The Sum of Squared Residuals of the regression.

outputStatistics

public boolean outputStatistics(java.io.File statsFile)

Specified by:

outputStatistics in interface StatisticalModel

outputStatistics

public java.lang.StringBuffer outputStatistics()

Specified by:

outputStatistics in interface StatisticalModel

read

public boolean read(java.io.File f)
             throws java.io.FileNotFoundException

Specified by:

read in interface Regressor

Throws:

java.io.FileNotFoundException

write

public boolean write(java.io.File s)

Description copied from interface: Regressor

Writes to the file specified

Specified by:

write in interface Regressor

Parameters:

s - the name of the file to write to

Returns:

returns true if all is OK

write

public boolean write(java.io.PrintStream out)

Description copied from interface: Regressor

Writes this Template to file (via a PrintStream)

Specified by:

write in interface Regressor

Parameters:

out - The output PrintStream

Returns:

if written successfully

read

public boolean read(iniFile in)

Description copied from interface: IniHandler

Read from the current position in an iniFile. assumes that the iniFile is already in the object's root block

Specified by:

read in interface IniHandler

Returns:

true if successfully read.

write

public boolean write(iniFile file,
                     java.lang.String name)

Description copied from interface: IniHandler

Writes this Template to file (via a PrintStream)

Specified by:

write in interface IniHandler

Parameters:

file - The output ini file to fill with class data

Returns:

if written successfully

getInputDimensions

public int getInputDimensions()

Specified by:

getInputDimensions in interface StatisticalModel

getModelInformation

public void getModelInformation(ModelInformation mi)

Description copied from interface: StatisticalModel

Queries the statistical model about its abilities.

Specified by:

getModelInformation in interface StatisticalModel

apply

public BigMat apply(BigMat in)

Description copied from interface: StatisticalModel

Apply the statistical model on the input data supplied in Matrix form

Specified by:

apply in interface StatisticalModel

Parameters:

in - matrix containing values for modeling

Returns:

values computed by the model.

getOutputDimensions

public int getOutputDimensions()

Specified by:

getOutputDimensions in interface StatisticalModel

getModelInformation

public void getModelInformation(ModelResults result)

Description copied from interface: StatisticalModel

Query the (previously built) model for information about the model's components

Specified by:

getModelInformation in interface StatisticalModel

Parameters:

result - ModelResults object to fill with appropriate information